diff --git a/slides/regularization/rsrc/enet_exp.R b/slides/regularization/rsrc/enet_exp.R
new file mode 100644
index 00000000..3980df8e
--- /dev/null
+++ b/slides/regularization/rsrc/enet_exp.R
@@ -0,0 +1,83 @@
+library(mlr3)
+library(glmnet)
+library(mlr3learners)
+library(mlr3tuning)
+library(mlr3misc)
+library(pracma)
+library(mvtnorm)
+
+n_train = 100
+n_test = 10000
+n = n_train + n_test
+n_reps = 20
+n_folds = 5
+gs1_grid = 30
+gs2_grid = c(10, 20)
+p = 500
+q_seq = c(5, 500)
+x_corr = 0.8
+
+tuner1 = tnr("grid_search", resolution = gs1_grid)
+tuner2 = tnr("grid_search", 
+  param_resolutions = c(alpha = gs2_grid[1], lambda = gs2_grid[2]))
+inner = rsmp ("cv", folds = n_folds)
+mm = msr("regr.mse")
+
+l1 = lrn("regr.glmnet", alpha = 0, id = "ridge")
+l2 = lrn("regr.glmnet", alpha = 1, id = "lasso")
+l3 = lrn("regr.glmnet", id = "enet")
+ss1 = ps(
+  lambda = p_dbl(1e-3, 1e2, logscale = TRUE)        
+)        
+l1 = auto_tuner(tuner1, l1, inner, mm, search_space = ss1)
+l2 = auto_tuner(tuner1, l2, inner, mm, search_space = ss1)
+ss2 = ps(
+  alpha = p_dbl(0, 1), 
+  lambda = p_dbl(1e-3, 1e2, logscale = TRUE)        
+)        
+l3 = auto_tuner(tuner2, l3, inner, mm, search_space = ss2)
+
+mylearners = list(l1, l2, l3)
+myrsmp = rsmp("holdout", ratio = n_train / n)
+# lrn_order = c("LM", "ridge", "lasso") 
+
+make_simul_data = function(rep_i, q) {
+  sigma = x_corr^(0:(p-1))
+  sigma = Toeplitz(sigma)
+  X = rmvnorm(n = n, sigma = sigma)
+  eps = rnorm(n = n, sd = 0.1)
+  theta = c(rep(1, q), rep(0, p-q))
+  y = X %*% theta + eps
+  d = as.data.frame(X)
+  colnames(d) = sprintf("x%03i", 1:p)
+  d$y = y
+  tt = as_task_regr(d, target = "y", id = sprintf("q:%i", q))
+  return(tt)
+}
+
+run_bm = function(n_reps) {
+  simul_grid = expand.grid(q = q_seq, rep_i = 1:n_reps)
+  mytasks = lapply(1:nrow(simul_grid), function(i) {
+    row = simul_grid[i,]                 
+    make_simul_data(rep_i = row$rep_i, q = row$q)
+  })
+  bg = benchmark_grid(mytasks, mylearners, myrsmp)
+  bmr = benchmark(bg, store_models = TRUE)
+  ba = bmr$aggregate(msr("regr.rsq"))
+  list(bmr = bmr, ba = ba)
+}
+
+future::plan("multicore")
+z = run_bm(n_reps)
+ba = z$ba
+bmr = z$bmr
+
+nn = length(bmr$uhashes)
+betas = lapply(1:nn, function(i) {
+  at = bmr$resample_results$resample_result[[i]]$learners[[1]]
+  gmod = at$learner$model
+  as.numeric(gmod$beta)
+})
+ba$betas = betas
+ba$resample_result = NULL
+save(file = "enet_exp.RData", bmr_aggr = ba)
diff --git a/slides/regularization/rsrc/enet_exp.RData b/slides/regularization/rsrc/enet_exp.RData
new file mode 100644
index 00000000..92e28ad9
Binary files /dev/null and b/slides/regularization/rsrc/enet_exp.RData differ